Accurate floor detection and segmentation for indoor navigation using RGB+D and stereo cameras

Real world indoor environments are rich in planar surfaces. Floor detection or ground-plane detection is a crucial requirement for a robotic navigation task. Despite frequent successes in this area, problems with detection of navigable floor with multiple planar and non-planar slopes at multiple heights still exist. For robust and safe navigation, such small variations such as floor joins, carpet deformities, raised steps and floor gradients need to be detected and robot path and kinodynamics plan must be adjusted accordingly. The authors suggest a recursive RANSAC segmentation based algorithm that estimates the dominant and sub-dominant plane models for all the navigable planes within a detected floor or a ground plane. The algorithm also divides the input point clouds intelligently into multiple regions of interest for both efficiency and accuracy enhancement. The recursive estimation approach for determining plane parameters helps to detect multiple planes within each region. Among other benefits of this approach, reduction of search space size for the estimation of plane parameters stands out to be the most striking result of this work. This region wise plane estimation approach also helps to reduce the computational load by selectively dropping less significant floor sections from estimation process. The floor estimation technique coupled with sensor response functions for two different point cloud generators further investigates into the robustness of the method when deployed on two distinct sensors i.e. RGB+D sensor and a stereo vision camera. In our experiments we segment navigable floor planes in realtime for a slowly moving sensor. The location and geometrical parameters of the floor planes are updated in a global coordinate systemwhenever a change their location is detected. The planes are associated to a grid map which serves as a path-planning reference to a mobile robot used in our experiments. The results of floor detection and the precision of floor anomaly detection are compared sensorwise and with the ground truth defined by obstacle heights and configuration.